Method and apparatus for remote sensing of objects utilizing radiation speckle

ABSTRACT

Disclosed are systems and methods to extract information about the size and shape of an object by observing variations of the radiation pattern caused by illuminating the object with coherent radiation sources and changing the wavelengths of the source. Sensing and image-reconstruction systems and methods are described for recovering the image of an object utilizing projected and transparent reference points and radiation sources such as tunable lasers. Sensing and image-reconstruction systems and methods are also described for rapid sensing of such radiation patterns. A computational system and method is also described for sensing and reconstructing the image from its autocorrelation. This computational approach uses the fact that the autocorrelation is the weighted sum of shifted copies of an image, where the shifts are obtained by sequentially placing each individual scattering cell of the object at the origin of the autocorrelation space.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a Continuation application of pending U.S. patentapplication Ser. No. 15/420,555 filed on Jan. 31, 2017; U.S. patentapplication Ser. No. 15/420,555 is a Continuation application of U.S.patent application Ser. No. 14/281,255 filed on May 19, 2014 now U.S.Pat. No. 9,582,883 issued Feb. 28, 2017; U.S. patent application Ser.No. 14/281,255 is a Continuation-in-Part of U.S. patent application Ser.No. 13/568,229 filed on Aug. 7, 2012 now U.S. Pat. No. 8,761,494 issuedJun. 24, 2014; U.S. patent application Ser. No. 13/568,229 is aDivisional application of U.S. patent application Ser. No. 11/764,196filed on Jun. 16, 2007, now U.S. Pat. No. 8,265,375 issued Sep. 11,2012; U.S. patent application Ser. No. 11/764,196 claims the benefit ofU.S. Pat. App. No. 60/814,149 filed on Jun. 16, 2006, U.S. Pat. App. No.60/816,305 filed on Jun. 23, 2006 and U.S. Pat. App. No. 60/816,982filed on Jun. 27, 2006; U.S. patent application Ser. No. 14/281,255 isalso a Continuation-in-Part application of U.S. patent application Ser.No. 13/189,349 filed on Jul. 22, 2011 now U.S. Pat. No. 8,736,847 issuedMay 27, 2014; U.S. patent application Ser. No. 13/189,349 claims benefitof U.S. Pat. App. No. 61/367,409 filed Jul. 24, 2010 and U.S. Pat. App.No. 61/435,283 filed on Jan. 22, 2011; U.S. patent application Ser. No.14/281,255 is also a Continuation-in-Part application of U.S. patentapplication Ser. No. 12/921,185 having a 371(c) date of Sep. 7, 2010 nowU.S. Pat. No. 8,810,800 issued Aug. 19, 2014; U.S. patent applicationSer. No. 12/921,185 is a 371 of PCT App. No. PCT/US09/37,999 filed Mar.23, 2009; PCT App. No. PCT/US09/37,999 claims benefit of U.S. Pat. App.No. 61/070,352 filed Mar. 22, 2008 and U.S. Pat. App. No. 61/115,923filed on Nov. 18, 2008; and all of said applications are hereinincorporated by reference in their entirety.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

This invention was made with Government support under Contract #FA8721-05-C-0002 awarded by the United States Air Force. The Governmenthas certain rights in the invention.

REFERENCE TO SEQUENCE LISTING, A TABLE, OR A COMPUTER PROGRAM LISTINGCOMPACT DISC APPENDIX

Not Applicable

BACKGROUND OF THE INVENTION 1. Field of the Invention

This invention involves the application of tunable radiation sources toremotely sense objects with opaque surfaces.

More specifically, this invention addresses the systems and methods toextract information about the object by observing variations of theradiation pattern caused by illuminating the object with coherentradiation sources and changing the outputting of wavelengths of thesource.

2. Description of the Prior Art

Multiple technologies and methods are used to sense and constructmulti-dimensional representations of remote objects. Commonthree-dimensional imaging solutions include triangulation-based laserscanners, scanning modulated waveform laser radars and imagingtime-of-flight laser radars. However, each of these technologies haslimitations.

Triangulation-based laser scanners direct a laser beam at the surfacefrom one end of the scanner and receive the reflected laser light at theother end of the scanner. By measuring the angle, the distance can becalculated. Although capable of high accuracy, they are of limited usefor long range scanners, because the longer the range, the larger thescanner needs to be. Shadowing effects also impact the ability of thistechnique to construct accurate representations of objects.

Scanning modulated waveform laser radars modulate a laser beam in aknown way and record the returning beam. By comparing the outgoing andincoming waveforms, the distance can be computed. This technique is arelatively slow point-by-point measurement technique requiring the beamto scan the object. This technique provides limited resolution.

Imaging time-of-flight laser radars direct a laser beam out towards theobject, then measure how long it takes for the light to return. By usingthe time taken for the response and the speed of light, the distance canbe calculated. Although time-of-flight solutions are capable ofoperating over very long distances, due to the high velocity of light,timing the round-trip time is difficult and the accuracy of the rangeresolution is relatively low.

It is also known that “speckle” can be used to obtain three-dimensionalinformation about a remote object. “Speckle” is an interferencephenomenon that occurs when coherent radiation (e.g., laser light) isreflected from a rough or multiply scattering sample onto a detectionplane. Due to scattering of photons from and within the sample,different photons travel different distances to the detection plane. Asa result, the light reflected or backscattered from the sample, iftemporally coherent, interferes at the detection plane, producing agrainy pattern known as “speckle.” Techniques exist to utilize specklepatterns, called speckle-pattern sampling, to obtain range informationfrom the wavelength of speckle as well as measure the spatial dependenceof the speckle pattern to resolve the object laterally. These specklepatterns can be detected with a sensor array at each of a set of equallyspaced laser frequencies. The individual frames are stacked to form athree-dimensional data array, and a three-dimensional Fourier Transform(FT) is performed on this data array. The FT process yields thethree-dimensional autocorrelation function of the three-dimensionalimage of the object. The use of “speckle” for three-dimensional imagingis a technique disclosed in U.S. Pat. No. 5,627,363 which is hereinincorporated by reference in its entirety. Techniques forspeckle-pattern sampling are also described in L. Shirley and G.Hallerman, “Technical Report 1025, “Application of Tunable Lasers toLaser Radar and 3D Imaging,” MIT Lincoln Lab Technical Report 1025, 26Feb. 1996, which is herein incorporated by reference in its entirety.

Although speckle-pattern sampling is an improvement in the art ofthree-dimensional imaging, there are still shortcomings to thistechnique. With speckle-pattern sampling, reference points and referenceplanes can be used to produce the three-dimensional image from theautocorrelation of an image. However, it is not always possible toimplement the reference-point techniques that currently exist in theart. There are situations where the imaging system may not be able topredetermine or place a reference point in proximity of the object.There are also situations where significant benefits can be gained froma movable and self contained imaging system that can create referencepoints.

Where reference points are not possible, and phase information of thereceived light is not known, speckle-pattern sampling requires demandingcomputational approaches for reconstructing the three-dimensional imagefrom its autocorrelation. It has been an ongoing challenge to determinemethods and systems to efficiently transform this autocorrelation datainto a representation of an object.

Therefore, there exists a need in the art for three-dimensional imagingsystems and methods that addresses these shortcomings.

BRIEF SUMMARY OF THE INVENTION

It is an object of this invention to provide systems and methods toextract information about the size and shape of an object by observingvariations of the radiation pattern caused by illuminating the objectwith coherent radiation sources and changing the wavelengths of thesource.

It is an object of the invention to provide imaging systems comprising asource of coherent radiation outputting a plurality of wavelengthsilluminating an object creating a plurality of speckle patterns eachcorresponding to the plurality of wavelengths, at least one sensor at alocation relative to the object to determine the intensity of thespeckle pattern for each of the plurality of wavelengths, a processorreceiving information from the sensor, the processor having means forconstructing at least one autocorrelation of the object from theintensities of the speckle patterns, a controller for controlling thesource of coherent radiation creating a focused laser beam for use as aprojected reference point and the processor further comprising means forexecuting a regional image selection technique to construct arepresentation of the object utilizing the projected reference point andthe autocorrelation.

It is another object of this invention to provide an imaging apparatuscomprising a source of coherent radiation outputting a plurality ofwavelengths illuminating an object creating a plurality of specklepatterns each corresponding to the plurality of wavelengths, at leastone sensor at a location relative to the object to determine theintensity of the speckle pattern for each of the plurality ofwavelengths, a processor receiving information from at least one sensor,the processor having means for constructing one or more autocorrelationsof the object from the intensities of the speckle pattern, the processorhaving means for receiving one or more speckle patterns influenced by areference point created by an area of a curved surface and the processorfurther comprising means for executing a regional image selectiontechnique to construct a representation of the object utilizing theprojected reference point and the autocorrelation.

It is another object of the invention to provide an imaging apparatuscomprising a source of coherent radiation outputting a plurality ofwavelengths illuminating an object creating a plurality of specklepatterns each corresponding to the plurality of wavelengths, at leastone sensor at a location relative to the object to determine theintensity of the speckle pattern for each of the plurality ofwavelengths, a processor receiving information from at least one sensor,a processor having means for constructing one or more autocorrelationsof the object from the intensities of the speckle pattern, and theprocessor further comprising means to compare at least one of theautocorrelations.

It is another object of the invention to provide a method to construct arepresentation of an object from speckle patterns comprising the stepsof shifting and comparing autocorrelation points of an autocorrelationwhere the autocorrelation comprises autocorrelation points that lie on acopy, the copy comprising autocorrelation points representing an objectand autocorrelation points that do not lie on the copy and eliminatingautocorrelation points that do not lie on the copy, constructing arepresentation of the object from the autocorrelation points that lie onthe copy.

It is another object of the invention to provide a method to construct arepresentation of an object wherein the method of shifting and comparingdescribed above further comprises the steps of selecting one or moreshift candidates comprising autocorrelation points that lie on the copy,shifting the autocorrelation to the shift candidates creating a shiftedautocorrelation, comparing the shifted autocorrelation with theautocorrelation and eliminating autocorrelation points that do not lieon the copy, creating a partial reconstruction of the copy, updating thepartial reconstruction of the copy with steps further comprising:selecting one or more additional shifts, shifting the autocorrelation tosaid additional shifts, creating a second shifted autocorrelation andcomparing the second shifted autocorrelation with the partialreconstruction of the copy, and eliminating autocorrelation points thatdo not lie on the copy, creating an update to the partial reconstructionof the copy; repeating the foregoing steps to update the partialreconstruction of the copy until the partial reconstruction of the copycomprises a representation of the object and outputting therepresentation of the object.

It is another object of the invention to provide an imaging apparatuscomprising a source of coherent radiation rapidly outputting a pluralityof wavelengths illuminating an object creating a plurality of specklepatterns each corresponding to the plurality of wavelengths, a sensor ata location relative to the object to determine the intensity of thespeckle pattern for each of the plurality of wavelengths, means toassociate the outputting of each of the plurality of wavelengths fromthe radiation source with the receipt of corresponding intensities ofthe speckle pattern for each of the plurality of wavelengths at thesensor and a processor receiving information from the sensor, andprocessor having means for constructing one or more autocorrelations ofthe object from the intensities of the speckle patterns.

It is an object of the invention to provide high lateral resolution ofimages without the need for large precision optics. In this invention,resolution can be determined by a sensor array size

It is a further object of this invention to provide enhanced rangeresolution of images through tuning the wavelength of coherent radiationsources. In this invention, range resolution can be determined by thetuning bandwidth.

It is another object of this invention to provide an imaging solutionthat has reduced sensitivity to atmospheric turbulence. This inventionis insensitive to turbulence at or near the sensor plane.

It is an object of this invention to provide an imaging solution thathas reduced laser coherence requirements. In this invention, phasecoherence is not necessary between laser frequency steps.

It is a further object of this invention to provide an imaging solutionthat has reduced sensor bandwidth requirements.

It is another object of this invention to provide an imaging solutionwith reduced sensor rigidity requirements compared to conventionalimaging solutions. In this invention, the sensor array need not berigid.

It is an object of this invention to provide an imaging solution for usewith a broad set of applications. Applications for this inventioninclude, but are not limited to applications such as biometrics,three-dimensional iris scanning, object recognition, long-rangedimensional metrology, remote inspection, and 3D microscopy.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING

FIG. 1a shows a schematic diagram of one embodiment of a system forimaging of objects according to the present invention;

FIG. 1b shows one embodiment of a graphic representation of collecteddata which includes a series of two-dimensional arrays with eachrepresenting observed intensity values at a particular wavelength;

FIG. 1c shows a representation of one embodiment of a sensor used tocollect speckle patterns and intensity values;

FIG. 2 shows a graphical representation of one embodiment of a radiationsource rapidly emitting a plurality of wavelengths, the source ofradiation being synchronized with a receiver creating data representingspeckle patterns and intensity values;

FIG. 3a shows a schematic diagram of one embodiment of a radiationsource projecting a reference spot;

FIG. 3b shows an autocorrelation with an offset region of theautocorrelation being produced by a reference point;

FIG. 4a shows one embodiment of constructing a virtual reference pointwith a transparent curved surface;

FIG. 4b represents the autocorrelation produced with a virtual referencepoint;

FIG. 5 shows the effect of a shift-and-compare operation on anautocorrelation;

FIG. 6 shows a process flow diagram of one embodiment of the method toreconstruct an image from an autocorrelation;

FIG. 7 shows a process flow diagram of one embodiment of the method toreconstruct an image from speckle patterns;

FIG. 8 shows a representative example of one embodiment ofshift-and-compare methods in this invention;

FIG. 9 illustrates a vector matching operation for determiningadditional shift candidates; and

FIG. 10 shows a representation of one embodiment of a consistency matrixand reordering of the consistency matrix.

DETAILED DESCRIPTION OF THE INVENTION

Described are a wide array of systems and methods for remote sensing ofobjects. However, it will be appreciated that the inventive conceptsdisclosed herein are not limited to the specific embodiments disclosed.For example, the general techniques disclosed herein may be usefullyemployed in any environment where precise, three-dimensional data mightbe usefully captured and processed, including ophthalmology, materialinspection, manufacturing, flight systems, surveillance, and remotesensing. In addition, while numerous variations and implementations ofremote sensing are described, it will be appreciated that othercombinations of the specific illuminating, sensing, processing, andreconstruction techniques described herein may be used, and that suchvariations are intended to fall within the scope of this disclosure.

Terms and Definitions

To provide greater clarity and ease of comprehension, as well as toavoid ambiguities in wording and confusion of nomenclature, thefollowing titles, terms and definitions are provided in addition to theterms as commonly used in the art.

Surface Scattering Function: A mathematical representation of an objectbased on the reflection of radiation off the surface of the object. Asufficient mathematical model for the purposes of this application is todescribe the surface of a three-dimensional shape by a complex surfacescattering function g(x, y, z). The support of the function, meaning thepositions in space where the function is non-zero, represents surfacelocations. The magnitude and phase of the function at these locationsrepresent, respectively, the scattering strength and the phase changeimparted on the reflected wave. Shadowing from the object is normallytaken into account so that the function is zero-valued for surfaces thatare not illuminated. The magnitude of the surface scattering functionmay also be adjusted to account for a non-uniform illumination beam.

Speckle Pattern and Speckle Pattern Intensity: Reflected radiation fromobjects with rough surfaces produces a speckle pattern whose intensitypattern varies spatially due to interference between differentcontributions to the optical field at a given observation point. Thespeckle pattern also varies with frequency v or wavelength λ of theilluminating laser. Thus, the intensity at an observation point can berepresented by the function I(x, y, z; v). A speckle pattern istypically, but not necessarily, measured on a nominally flat surface inspace. If there is no relative motion or atmospheric turbulence, thenthe speckle intensity is constant in time.

Data Cube: The speckle pattern intensity sampled at positions in spaceat different values of laser frequency forms a data cube. Typically, andin this description, a data cube refers to the speckle pattern intensitymeasured on a detector array at discrete values of laser frequency. Adata cube need not have the same number of elements in each of the threedimensions. The term data cube may refer to a set of speckle patternintensity data that is remapped onto a three-dimensional array in orderto more uniformly sample Fourier space on a regular grid (see“Applications of Tunable Lasers to Laser Radar and 3D Imaging,” L. G.Shirley and G. R. Hallerman, MIT Lincoln Laboratory Technical Report1025, 26 Feb. 1996, for a discussion of sampling Fourier space).

Autocorrelation: The autocorrelation P(x, y, z) of the complexthree-dimensional function g(x, y, z) is defined mathematically as:

P(x, y, z) = ∫_(−∞)^(∞)∫_(−∞)^(∞)∫_(−∞)^(∞)g ⋆ (x^(′), y^(′), z^(′))g(x + x^(′), y + y^(′), z + z^(′),)dx^(′)dy^(′)dz^(′).

This mathematical definition describes the autocorrelation of a functionas being the sum of a series of copies of the function with each pointin the function being placed sequentially at the origin of theautocorrelation coordinate system and weighting the copies by thecomplex conjugate of the value of the function at the point that isplaced at the origin. It is known that within certain approximations,the three-dimensional Fourier transform of a data cube provides anestimate of the autocorrelation P(x, y, z) of the surface scatteringfunction representing the three-dimensional shape of the target. Ingeneral, the autocorrelation function is complex valued. Here, we aremainly interested in the support (meaning positions where the functionis non-zero) and the magnitude of the autocorrelation function at thesesupport points. Therefore, we use the term autocorrelation function todenote the magnitude of P(x, y, z). The term autocorrelation alsoincludes averaging the autocorrelation, meaning the average of aquantity related to the magnitude of P(x, y, z), for a set of data cubesfrom different realizations of the speckle pattern intensity. Therealizations may be obtained, for example, from slightly differentviewing geometries, often produced by a small relative rotation of theobject, or by sampling Fourier space at different beginning wavelengths.For example, a large data cube may be divided into two or more smallerdata cubes for the purpose of averaging. Two examples of averaging aretaking the average of the magnitude and the root-sum-square average ofthe magnitude of P(x, y, z). Given a particular copy of the surfacescattering function, points on the support of the autocorrelation can bedivided into two groups: autocorrelation points that lie on the copy,and autocorrelation points that do not lie on the copy. The support ofthe autocorrelation contains the complete copy.

Copy and Inverted Copy: A copy is a translated version of the function gsuch that a support point of g lies on the origin. In this description,a copy also refers to the point-by-point magnitude of the translatedcopy. If we think of the surface scattering function as comprising a setof N discrete scattering points denoted by amplitudes g₁, g₂, . . .g_(N) occurring at the positions (x₁, y₁, z₁), (x₂, y₂, z₂), . . .(x_(N), y_(N), z_(N)), then we can write

${{P\left( {x,y,z} \right)} = {\sum\limits_{n = 1}^{N}{g_{n} \star {g\left( {{x + x_{n}},{y + y_{n}},{z + z_{n}}} \right)}}}},$

which can be interpreted as N shifted copies of the scattering functiong. The nth copy is obtained by shifting the scattering function suchthat point n lies at the origin. The copy is weighted by the complexconjugate g*_(n) of the amplitude of the point that is set at theorigin. (In practice, it is sufficient to represent a continuous surfaceas a set of discrete points since all three-dimensional images havepractical resolution limits.) Alternatively, P(x, y, z) may be writtenas

${{P\left( {x,y,z} \right)} = {\sum\limits_{n = 1}^{N}{{g_{n}g} \star \left( {{{- x} + x_{n}},{{- y} + y_{n}},{{- z} + z_{n}}} \right)}}},$

which can be interpreted as N inverted and complex conjugated copies ofthe scattering function, where inverted refers to flipping the sign ofthe function in all three dimensions. Thus, we can think of P(x, y, z)as being comprised of multiple shifted copies or of multiple shiftedinverted complex conjugated copies of the scattering function. Thedifferent embodiments of the invention relate to forming athree-dimensional image by extracting a copy or an inverted copy of thescattering function from the autocorrelation function. Since the phaseof the scattering function g is relatively unimportant in representing athree-dimensional image, we use the term copy to refer to an estimate ofthe support and magnitude of g. The primary objective ofthree-dimensional imaging is to determine the location of the surface orthe support of g. A secondary objective is to estimate the magnitude ofthe scattering function on the surface. We will not distinguish betweencopy and inverted copy unless it is necessary to do so. We willtypically consider the reconstruction of either the copy or the invertedcopy to be a solution since one can be obtained from the other.

Interference Events: Light scattered from two points on the surface ofthe object interferes to produce a corresponding contribution at twopoints in the autocorrelation. If a vector is formed whose end pointsare the two scattering points, then the position of one of thecorresponding points in the autocorrelation is determined by placing oneend of the vector on the origin of the autocorrelation. The second pointin the autocorrelation, which is symmetric about the origin, isdetermined by placing the other end of the vector at the origin. Theterm interference event refers to the interference of two points on theobject producing a given point in the autocorrelation function. If agiven point in the autocorrelation is produced by only one pair ofpoints on the object, then it is termed a single interference event. Ifa point in the autocorrelation function is produced by multipleinterference events, then these individual contributions interfereproducing a point on the autocorrelation function whose magnitudedepends on the relative phases and strengths of the individualcontributions. Thus, different speckle realizations may produce widelyvarying magnitudes at points where multiple interference events occur.Some of these points may have very low values, or dropouts, that aremasked by noise. Averaging the autocorrelation as described above tendsto fill in these dropouts. One can locate points in the autocorrelationfunction arising from single interference events or a low number ofinterference events by calculating the variation of the estimates usedto form the averaged autocorrelation function. Points arising fromsingle interference events tend to have lower variance. Points near theorigin of the autocorrelation tend to be produced from many interferenceevents.

Brightness: The magnitude of a point in the autocorrelation relative toother points in the autocorrelation. It also refers to the relativemagnitude of autocorrelation points resulting after one or moreshift-and-compare operations.

Bright Copies: Copies that are produced in the autocorrelation byplacing bright points in the surface scattering function at the origin.Bright points in the autocorrelation that are not too close to theorigin tend to be produced by interference events involving brightpoints on the surface scattering function.

Projected Reference Point: A spot of focused coherent radiation that isprojected onto the surface of the object in addition to a wider-areaillumination beam, the illumination beam being created by a source ofcoherent radiation. The projected reference point may be an illuminationspot of reduced size on an object relative to an illumination beam, orillumination spot on said object. The projected reference pointinfluences the speckle pattern intensity in a manner that carries usefulinformation. The spot can be formed by a means of controlling theillumination beam to produce a separate, optical beam that arrives atthe surface of the object at a delayed or earlier time than thewider-area illumination beam. The means of controlling the illuminationbeam can include a lens to move and focus the beam and the means ofcontrolling can also include a means for optically splitting a singlesource of radiation creating two beams and delaying one beam'sillumination of the object relative to the other beam. The position ofthe projected reference point on the object can also be varied by movingthe source of radiation relative to a focusing element. For example, ifthe source of radiation is the tip of a fiber, one means for moving theprojected reference point on the surface of the object is to move thetip of the fiber sideways with a piezoelectric positioner. The projectedreference point produces a copy in the autocorrelation that is separatedfrom the central area of the autocorrelation. If the time delay betweenthe illumination beam and the beam forming the projected reference spotis greater than twice the corresponding range extent of the illuminatedregion of the object, then the copy produced by the projected referencepoint will be completely separated from and distinct from the centralregion of the autocorrelation.

Curved Surface Creating a Reference Point: A surface that reflects aportion of an illumination beam while allowing most of the illuminationto strike the object, the illumination beam being created by a source ofcoherent radiation. The curved surface produces a virtual referencepoint that is offset from the object in range and that influences thespeckle pattern intensity in a manner that carries useful information.Provided that the curved surface is sufficiently far away from theobject, the virtual reference point forms a distinct copy in theautocorrelation that is separated from the central region of theautocorrelation.

Regional Image Selection Technique: The process of forming athree-dimensional image of an object without performing a reconstructionby selecting a region in the autocorrelation function that contains acopy that is separated and distinct from the central region of theautocorrelation. The copy is typically formed by a real or virtualreference point that is offset in range. The copy is offset from thecentral region of the autocorrelation and can be selected by croppingthe autocorrelation to include only the copy.

Change Detection: The process of comparing autocorrelations of twosimilar objects or of a single object at different times and observingdifferences in the autocorrelations. These differences in theautocorrelation are related to changes in the object or variationsbetween objects.

Rotate and Compare: The process of obtaining autocorrelations of anobject at slightly different angles and rotating one autocorrelationwith respect to the other by the angle between the two measurements suchthat the two autocorrelations overlap. The two autocorrelations are thencompared point by point. Regions that are supported in oneautocorrelation and not the other are related to points that have becomeshadowed or have become visible due to the relative rotation of theobject between measurements. Varied support regions may also be due tospecular returns that are present for only one of the measurements. Forexample, if a bright point or specular reflection occurs in onemeasurement and not the other, then the difference between these twomeasurements will provide an estimate of a copy.

Physical Feature of an Object: A structure in similar objects thatvaries between these objects and produces differences in theautocorrelation that can be measured without performing areconstruction. Identification, recognition, or classification of theseobjects can be performed based on comparing the differences in theautocorrelation arising from differences in the features occurringbetween objects. Physical features can be localized or distributed overthe surface. A localized feature of an object is termed a partialrepresentation of the object. It can be determined that a givenlocalized feature is not contained in the object if the autocorrelationdoes not contain all of the support points of the autocorrelation of thefeature taken by itself. Rotating and comparing may be required to alignthe coordinate systems.

Time Gating: The process of gating a receiver or sensor in conjunctionwith a pulsed laser train in a manner that blocks out backscatteredradiation in range intervals not associated with a region of interest.For example, background radiation that overfills an object and strikes abackground surface occurring at a greater range may be removed by timegating. In addition, certain range slices of the object may be isolatedby time gating. Isolating range slices of a controlled width oftenproduces regions of interest on the object with spatial holes becausecorresponding regions of the surface fall outside of the range slice.Surfaces with holes produce more sparsely populated autocorrelationsthat simplify the process of reconstructing a copy from theautocorrelation. Individual range slices can be reconstructed separatelyand then combined together to form an image with high range resolution.

Shift-and-Compare: The process of selecting a support point of theautocorrelation, shifting the origin of the autocorrelation to thispoint and doing a point-by-point comparison between the shifted and theoriginal autocorrelations. It can be proven that the overlap of thesupport of the shifted and original autocorrelation function contains atleast one intact copy and one intact inverted copy. The termshift-and-compare also refers to the process of comparing the shiftedautocorrelation with the result of a previous shift-and-compareoperation. It also refers to a series of shift-and-compare operationswhether performed one at a time sequentially or in parallel. Thecomparison operation may comprise decision rules for determining when todiscard points and how to adjust the brightness of a partialreconstruction. If a shift lies on a copy, then a shift-and-minoperation tends to eliminate points not lying on the copy whilepreserving points that do lie on the copy. A shift-and-min operationtends to refine a partial reconstruction if the shift is a good shift.

Partial Reconstruction: A resulting set of autocorrelation points thathave survived all prior shift-and-compare operations. The objective of aset of shift-and-compare operations is to form a partial reconstructionthat approximates a copy and can be used as a representation of theobject.

Shift: The term shift used as a noun refers to the point on the supportof the autocorrelation to which the origin is translated, or offset,before comparing in a shift-and-compare operation.

Shift: The term shift, when used as a verb, refers to the offsetting ofa point relative to another point. In this description, shift iscommonly used to define the action of offsetting the origin of anautocorrelation to another autocorrelation point.

Initial Shift: The first shift used in a sequence of shift-and-compareoperations.

Shift Candidates: Autocorrelation points on the support of theautocorrelation that are being considered for future shifts. Shiftcandidates must have survived all previous shift-and-compare operationsand therefore are taken from the list of points in the partialreconstruction. The crux of forming a partial reconstruction thatapproximates a copy is to select shift candidates to be used as shiftsthat lie on the copy being reconstructed. In the noiseless case, it canbe shown that all points on the copy survive shifts selected from thecopy being reconstructed. Thus, as additional shifts causeautocorrelation points not on the copy to be discarded or eliminated,the partial reconstruction tends to approach a copy.

Good Shift Candidate: A point from a list of shift candidates that lieson the copy being reconstructed, also referred to as a good shift.

Intersect Operation: The operation of taking the intersection or thelogical “and” of the set of shifted autocorrelation points and thepartial reconstruction (or the autocorrelation for a first shift) thatoverlap during a shift-and-compare operation. This operation isespecially amenable to rapid processing using single bit representationsof the autocorrelation, where brightness values above and below athreshold are set to 1 and 0, respectively.

Minimum Operation: The operation of taking the minimum of the set ofautocorrelation points that overlap during a shift-and-compareoperation.

Brightness-Ranking Operation: The operation of ranking, by brightness,the autocorrelation points that overlap during a set ofshift-and-compare operations. The decision to discard or eliminate anautocorrelation point can be delayed in order to mitigate noise. Forexample, a point might be discarded after a given number ofshift-and-compare operations has occurred and a certain percentile ofthe ranking has fallen below a threshold value.

Shift-and-Intersect: A shift-and-compare operation where the comparisonis an intersect operation. By intersect operation is meant theconventional mathematical intersection of two sets. Ashift-and-intersect operation is useful for rapidly exploring choices ofshifts and for finding initial shift candidates with few survivors.

Shift-and-Min: A shift-and-compare operation where the comparison is aminimum operation. By minimum operation is meant the conventionalmathematical minimum of a set of numbers corresponding to brightness.This operation tends to form a brightness distribution that correlateswith the magnitude of the surface scattering function. The processcannot be continued indefinitely, however, because as dimmer points onthe copy are used as shifts, the brightness of the partialreconstruction decreases and “melts” into the noise floor. Therefore,the shift-and-min operation is typically applied to brighter shifts.

Shift-and-Rank: A shift-and-compare operation where the comparison is abrightness-ranking operation. A brightness ranking consists of sortingpoints according to brightness. This operation also tends to form abrightness distribution that correlates with the magnitude of thesurface scattering function but is more forgiving of noise.

Survivors: Autocorrelation points that are not eliminated by ashift-and-compare operation or a set of shift-and-compare operations.

Breaking Symmetry: Choosing a good shift candidate that lies on eitherthe copy or inverted copy but not both and then running that shift toeliminate points on the copy not being selected. Since the initial shiftproduces both a copy and in inverted copy, a decision needs to be madeabout which one to reconstruct. The copy and inverted copy are pointsymmetric to within an offset so that the copy can be calculated fromthe inverted copy and vice versa. Thus, it is not important in thereconstruction process whether the copy or inverted copy isreconstructed.

Column: The z dimension in the autocorrelation or a partialreconstruction corresponding to range, where the range directioncorresponds to the direction between the receiver, or sensor, and theobject. The term column also refers to a column in a data cube, with theposition in the column corresponding to wavelength or frequency.

Preferred Shift Candidates: Shift candidates that are selected accordingto rules and that are likely to be effective at removing points in thepartial reconstruction that do not lie on the copy being reconstructedwhen performing a shift-and-compare operation. For example, the initialshift may be selected from a list of preferred shift candidates obtainedfrom the entire autocorrelation, and the list of preferred shiftcandidates for subsequent shift-and-compare operations may be selectedfrom the intersection of the partial reconstruction and the originallist of preferred shifts. Additionally, new preferred shift candidatesnot in the above list may be determined at any step of the process.Following are some illustrative examples of rules or procedures forgenerating lists of preferred shift candidates.

Locating Local Peaks: Locating autocorrelation points in theautocorrelation or the partial reconstruction that are brighter than allnearest-neighbors.

Locating Separated Peaks: Locating autocorrelation points with peaks inthe autocorrelation or the partial reconstruction that are greater thanall neighbors within a given radius or that are greater than any localpeaks within a given radius.

Locating Perimeter Points: Locating autocorrelation points on or nearthe perimeter of the support of the autocorrelation whose brightness isabove a threshold.

Locating Extreme Perimeter Points: Locating perimeter points that arefurther away from the origin than neighboring perimeter points or thathave larger |x|, or |y|, or |z| values than neighboring perimeterpoints.

Locating Potent Points: Locating autocorrelation points that when usedas an initial shift produce a relatively small number of survivors.

Locating Spreading Points: Locating autocorrelation points that whenused as an initial shift produce a large separation between the copy andinverted copy.

Locating Low Interference Points: Locating autocorrelation points in theautocorrelation that arise from a single interference event or a lownumber of interference events.

Opacity Constraint: A mathematical representation of the reflectiveproperties of a surface where the scattering function is a single-valuedfunction of x and y. An object with an opacity constraint is one whosesurface scattering function is a single-valued function of x and y. Ifthe sampling resolution in x and y is sufficiently fine compared to thesampling resolution in z (or alternatively the surface slope is not toolarge) then each x-y cell in the measurement will have contributionsfrom only one range cell. In this case the, the support of theautocorrelation along the z-axis is limited to the origin. Using thisfact, one can determine from inspection of the autocorrelation whetherthe opacity constraint is realized. If it is, then it can be used tohelp determine good shifts. For example, if there is only one supportpoint in a column of a partial reconstruction and it is known that thepartial reconstruction should have a surface at that location, then thesupport point must be a good shift.

Geometrical Assumptions: Assumptions about the shape of the object thatcan be helpful in the selection of good shifts. For example, if theobject is composed of geometrical elements, including, for example,planes, cylinders, cones, and spheres, then once these surface elementsbegin to appear in the partial reconstruction, new shift candidates canbe generated by continuing these shapes by interpolation orextrapolation and finding points on the partial reconstruction that lieon or near these surfaces. Furthermore, if a CAD model (or anymathematical model) of the object or features on the object exists, thennew shift candidates can be generated once features in the CAD modelbegin to appear. Additionally, a previous measurement of the object canbe used as a mathematical model upon which to select new shiftcandidates based on finding points in the partial reconstruction thatmatch the model.

Cluster of Shift Candidates: A set of shift candidates known to containat least one good shift.

Vector Matching: A method for selecting a cluster of shift candidates.The method consists of selecting a vector formed by the origin and asupport point of the autocorrelation and tagging those points in thepartial reconstruction where the vector endpoints fit into the supportpoints of the partial reconstruction. One method for obtaining andtagging these fit points is to sequentially place one end of the vectoron each support point of the partial reconstruction. If the other end ofthe vector aligns with a support point of the partial reconstructionthen both points in the partial reconstruction are tagged. Typically thesupport point is chosen to be a perimeter point or an extreme perimeterpoint in order to produce a vector that fits into a minimum number oflocations in the partial reconstruction. Since the support point in theautocorrelation from which the vector is formed arises from aninterference event, the vector must fit into the partial reconstructionin at least one place, and at least one of these fits must lie on thecopy. Therefore, it is guaranteed that the process produces a cluster ofshift candidates.

Union Operation: The operation of taking the point-by-point union orlogical “and” of a set of shift-and-compare operations. It is applied toa cluster of shifts to ensure that the no good points are eliminated. Apoint must be eliminated by all the shift-and-compare operations inorder to be eliminated from the partial reconstruction. This operationis especially amenable to rapid processing using single bitrepresentations of the autocorrelation, where brightness values aboveand below a threshold are set to 1 and 0, respectively.

Maximum Operation: The operation of taking the point-by-point maximum ofa set of shift-and-compare operations. It is applied to a cluster ofshifts to ensure that the no good points are eliminated. A point must beeliminated by all the shift-and-compare operations in order to beeliminated from partial reconstruction.

Start: A set of good shifts lying on a copy. Typically, the startconsists of a small number of shifts. If it is not clear how to proceedor the start lies on a copy that is not a bright copy, then it may beadvantageous to stop the process and save the start for later use.Individual starts may be combined and starts, or combined starts, may betranslated to bright copies to mitigate noise.

Self-consistent points: A set of autocorrelation points thatcollectively survive a set of shift-and-compare operations when allpoints are used as shifts. Although self consistency does not guaranteethat all points lie on the same copy, a large set of self-consistentpoints is likely to lie predominantly on the same copy.

Consistency Matrix: A matrix indicating the self-consistency of allpossible combinations of point pairs in a list of shift candidates. Thematrix may be formed by assigning an index number to each point in thelist and using these indices as row and column headings of atwo-dimensional array. If a point pair is self-consistent, then thecorresponding element of the array is set to 1 or True. If the pointpair is not self-consistent, then the corresponding element of the arrayis set to 0 or False. The rows or columns of the array can be generatedefficiently by doing a shift-and-intersect operation for thecorresponding shift and seeing which of the points on the list survive.

Matrix Reordering: The process of changing the order of the elementsmaking up the row and column headings of a consistency matrix so as tocreate a large block of self-consistent points.

Error Testing: The process of testing whether the partial reconstructionis mathematically consistent with the autocorrelation. In some cases itmay be necessary to choose shifts that are not guaranteed to be goodshifts. In this case, it may be necessary to perform a test to try todetermine if one or more bad shifts have been used. Then the process canbe stopped and picked up before the error occurred so that a new pathfor choosing shifts can be explored. Error testing can take many forms.One method is to test whether the support of the autocorrelation of theset of shift points produces points that are not in the autocorrelation.Another method is to calculate the autocorrelation of the partialreconstruction to see if all points in the measured autocorrelation arespanned by this new autocorrelation. This approach becomes moreeffective later in the process after the number of points in partialreconstruction has been reduced. Yet another approach is to test whetherthe extent of the partial reconstruction along each of the three axes issufficiently large to span the width of the autocorrelation in the samedirection.

Cross Matching: The process of comparing two or more starts andcalculating an offset between them that puts the shifts corresponding tothese starts on the same copy, thus combining two or more starts into asingle larger start. The entire start can now be run as shifts in ashift-and-compare operation, producing a partial reconstruction havingfewer extra points.

Translating Starts to a Bright Copy: The process of translating a startor a combined start so that it lies on a bright copy, which may be,ideally, the brightest copy.

Range Filtering: The process of eliminating extra points along a columnin a partial reconstruction after the final shift-and-compare operationhas been performed. If the brightest copy is being reconstructed, thenit is likely that the brightest residual point in the column occurs atthe correct z value in the column for the copy being reconstructed.

Processor: A processor, as used in this description is defined ashardware, software, or any combination of these suitable for processingthe three-dimensional imaging and reconstruction means and methodsdescribed herein. This includes realizations in one or moremicroprocessors, microcontrollers, embedded microcontrollers,programmable digital signal processors or other programmable device,along with internal and/or external memory. This may also, or instead,include one or more application specific integrated circuits,programmable gate arrays, programmable array logic components, or anyother device or devices that may be configured to process electronicsignals. It will further be appreciated that a realization may includecomputer executable code that may be stored, compiled or interpreted torun on one of the above devices, as well as heterogeneous combinationsof processors, processor architectures, or combinations of differenthardware and software. At the same time, processing may be distributedacross devices such as a radiation source, a sensor and/or computer in anumber of ways or all of the functionality may be integrated into adedicated, standalone image capture device. All such permutations andcombinations are intended to fall within the scope of the presentdisclosure.

DESCRIPTION

In reference to FIG. 1a , one embodiment of the invention includes atunable laser 100 illuminating object 105, whose three-dimensional shapeis being measured, with a laser beam 108. Laser beam 108 may overfillobject 105 or may spot illuminate a region of interest on object 105.Laser frequency 110 of laser beam 108 varies in time so as to produce aspatially varying speckle pattern intensity on detector array 115.Processor 120 receives speckle pattern intensity measurements fromdetector 115 and processes this information from detector array 115 toproduce a digital representation of the three-dimensional image ofobject 105. In reference to FIG. 1b , intensity measurements fromdetector array 115 are converted into a data cube 125 of intensityvalues at a discrete set of laser frequencies. The three dimensionalFourier transform of data cube 125 is calculated by processor 120 andproduces an estimate of the autocorrelation of the surface scatteringfunction representing the three-dimensional shape and scatteringproperties of object 105. In one embodiment, laser 100 is a CW laser andfrequency 105 is stepped approximately linearly in time. In anotherembodiment, laser 100 is a CW laser and frequency 110 is scannedapproximately linear in time. In yet another embodiment, laser 100 is apulsed laser and frequency 110 steps approximately linearly betweenpulses or sets of pulses. In a further embodiment, laser 100 is pulsedand detector array 115 is time gated. Time gating allows light in rangeintervals not associated with a region of interest to be blocked, thusminimizing clutter or selecting particular range slices of the target.In yet another embodiment, multiple laser frequencies are emittedsimultaneously and detector 115 contains wavelength selective elementsto cause different detector elements in detector array 115 to besensitive to different laser frequencies. In one embodiment, tunablelaser 100 is an external cavity diode laser, in another embodiment laser100 is a frequency modulated laser. In yet another embodiment, laser 100comprises a set of lasers operating at a set of discrete wavelengths.

Although the source of coherent radiation in one embodiment compriseslaser radiation creating laser beams, it is understood that theinvention is capable of utilizing other coherent radiation sources suchas, but not limited to all forms of electromagnetic radiation,ultrasonic radiation, and acoustic waves.

In reference to FIG. 1c , in one embodiment, detector array 115comprises a front structure 130 that contains an array of lenses 135.Lenses 135 may be ordinary lenses, or in order to reduce weight, size,and cost may be flat-panel lenses including, for example diffractivelenses, holographic lenses, or Fresnel lenses. Lenses 135 condense thespeckle pattern intensity at the various regions of front structure 130to fill detector arrays 140 which are distributed in a matching array onback structure 145. Front structure 130 and matching back structure 145need not be square and need not comprise rectangular arrays of lenses135 and detector arrays 140. In one embodiment, front structure 130comprises only one lens 135 and back structure 145 comprises only onedetector array 140. Condensing of the speckle pattern may be achieved,for example, by using a separation between front structure 130 and backstructure 145 such that the light entering lens 135 does not come to afocus on but fills detector 140. The advantage of using an array ofdetectors 140 is that the speckle pattern intensity distributed over thelarge of front structure 130 can be mapped onto a number of small andefficient detector arrays 140 with many detector elements. Lenses 135 ordetector arrays 140 may include wavelength selective filters or coatingsthat block light with wavelengths that lie outside of the tuning rangeof the laser, thus reducing background noise due to ambient light. Backstructure 145 may translate laterally 150 relative to front structure130 to effectively steer the receiver array and achieve fine pointingcontrol without rotating the entire structure.

Although embodiments of the system herein describe detectors 140 asbeing optical devices, detectors 140 may be any individual, array or anycombination of detectors, sensors or other devices capable of detectingradiation at the operational wavelength of radiation source 100. In oneembodiment, detectors 140 are CCD arrays, in another embodiment,detectors 140 are CMOS arrays, in yet another embodiment, detectors 140are electron-multiplied CCD arrays. In one embodiment, detector arrays140 comprise Geiger-mode avalanche photodiode arrays.

In one embodiment, laser 100 rapidly emits a plurality of wavelengths.Rapid emission of a plurality of wavelengths may be necessary in caseswhere there is a relative rotation between laser beam 108 and object 105during laser frequency scan 110 or where atmospheric turbulence causesthe speckle pattern to fluctuate on the time scale of frequency scan110.

FIG. 2 illustrates one embodiment of a system and method for use inspeckle imaging that can provide the type of data to be used in thisinvention. This embodiment of the invention employs a frequency scan 110that is a repetitive rapidly changing waveform 205. Rapid repetition ofthe waveform allows for autocorrelations to be produced from singlewaveform elements consisting of up-chirps 210 and down-chirps 215 orfrom combinations of small sets of these waveform elements in timeframes that are less than the speckle coherence time produced byrelative target rotations or atmospheric turbulence. Theseautocorrelations can be averaged to build up the signal-to-noise ratioof the resulting averaged autocorrelation. Waveform elements 210 and 215may exist in any combination and waveform 205 may be comprised entirelyof up-chirps 210 or down-chirps 215. In one embodiment, waveforms 205change frequency approximately linearly in time. In another embodiment,waveforms 205 are stepped in frequency. In yet another embodiment, laser100 is pulsed and waveforms 205 vary with approximately linear frequencysteps between pulses or sets of pulses.

In one embodiment, detector arrays 140 are avalanche photodiode arraysthat count photons. In another embodiment detector arrays 140 areavalanche photodiode arrays with timing circuits that mark the time ofarrival of the first photon. Avalanche photodiode arrays allow for verysensitive detection at single-photon levels. In one embodiment, the timeof arrival of the first photon in a given pixel of the detector arrayduring the time period 220 where the detector is armed and counting iscorrelated with the frequency or wavelength of that photon by noting inwhich time bin 225 the photon arrived at that pixel. Thus wavelengthinformation can be obtained from timing without requiring that a frameof speckle data be read out for each laser wavelength. This embodimentallows for very rapid acquisition of data cubes 230 at very low photoncounts. Each column in data cube 230 corresponds to a pixel in detectorarray 140. The blackened voxels 235 in data cube 230 represent thepixels and timing bins where a photo-electron is detected. The size ofdata cube 230 can be extended laterally by combining data cubes 230corresponding to the different detector arrays 140. In some types ofavalanche photodiode arrays, individual pixels that have produced aphoto-electron can be rearmed during time period 220 so that photons atmultiple wavelengths can be detected at a single pixel during timeperiod 220. In another type of avalanche photodiode array, a pixel thathas detected a photo-electron is dead until the entire array is reset atthe next cycle corresponding to a new time period 220. In this case,only one photo-electron can be detected per column of the data cube andthe probability of detection decreases towards the right end of timeperiod 220 because of this so-called saturation effect. In oneembodiment, the saturation effect is mitigated by combining the countsfrom both an up-chirp 210 and a down-chirp 215 so that the probabilityof detection for the combination 205 is more evenly distributed overdifferent laser frequencies making up waveform 205. In anotherembodiment, there are extra pixels in detector array 140 so that anindividual speckle is over sampled. Thus, the resulting macro-pixel thatis formed by combining results for a given range bin in a block ofpixels can have more than one count. In one embodiment a data cube isgenerated from multiple timing cycles. The number of time periods 220that can be combined into a single data cube is determined from thetime-scale of the speckle fluctuations due to influences such asrelative target rotation and turbulence.

A three-dimensional Fourier transform of each data cube 230 is generatedby a processor in order to produce an estimate of the autocorrelationfor averaging. Methods of generating Fourier transforms are well knownin the art and all are contemplated for use in this invention. In oneembodiment, averaging is achieved by calculating the root-mean-squareaverage of the magnitudes of the individual Fourier transforms of datacubes 230. In another embodiment, the average of the magnitudes of theFourier transforms are calculated. In one embodiment, the Fouriertransforms are calculated via the Fast Fourier Transform (FFT)algorithm. In another embodiment the Fourier transforms are calculatedvia a Discrete Fourier Transform (DFT), which may be more efficient whenthe total number of photo-electron counts in a data cube is small.

In one embodiment of the invention, motion of the speckle patternintensity arising from relative rotation of object 105 with respect tolaser beam 108 is compensated for by remapping the speckle patternintensity within a data cube to account for motion of the specklepattern intensity during a frequency scan 110. This mapping may occurbetween elements of the composite data cube arising from individualdetector arrays 140. The primary effect of relative rotation is to shiftthe speckle pattern intensity across detector 115. Remapping of thespeckle pattern intensity reduces the requirements on the speed of laserfrequency scan 110 and allows more time periods 220 to be combinedcoherently to produce data cubes 230.

In another embodiment of the invention, motion of the speckle patternarising from relative rotation of object 105 with respect to laser bean108 is used to synthesize a larger effective aperture. Thus, the lateralresolution of the three-dimensional image can be extended beyond thesize limitations of detector array 115.

In another embodiment, the speckle pattern intensity is remapped whenforming a data cube to more linearly sample Fourier space. For example,speckle size is proportional to the laser wavelength and rescaling thespeckle size so that it remains constant during a scan improves thelateral resolution of the three-dimensional image.

FIGS. 3a, 3b, 4a and 4b illustrate embodiments of the systems andmethods that provide for projected and virtual reference points that canbe used by a regional image selection technique to construct arepresentation of an object.

Referring to FIG. 3a , in one embodiment of the invention, projectedreference point 300 is projected onto the surface of object 105 inaddition to laser illumination from laser beam 108. Projected referencepoint 300 is formed by optically splitting light from laser 100 intopath 305 and path 310, delaying one path with respect to the other bycausing it to follow a longer path, and focusing one path to produceprojected reference point 300. In one embodiment spitting isaccomplished by coupling laser 100 into fiber 315 and using fibersplitter 320 to produce paths 305 and 310. The delay 325 is produced byusing different lengths of fiber before recombining the beams. In oneembodiment the beams are recombined by reflecting the light from path325 off of pin-hole mirror 330 and directing light from path 305 throughthe hole in pin-hole mirror 330. Fiber ends 335 and 340 are positionedsuch that light from both paths passes through lens 340 and one pathproduces projected reference spot 300 while the other path producesilluminating laser beam 108. In another embodiment, paths 305 and 310 donot overlap at the transmitter. Fiber ends 335 and 340 are offsetlaterally with each beam passing through its own lens such as to produceprojected reference spot 300 and illumination beam 108. In oneembodiment lens 345 is replaced with a curved mirror. Likewise, in theembodiment using two different lenses, one or both of these lenses maybe replaced with a curved mirror. This embodiment shows an example ofsources of the coherent radiation outputting the wavelengths being fiberends 335 and 340 each outputting the wavelengths.

Due to the optical path delay associated with projected reference point300, the light backscattered from the region of object 105 illuminatedby projected reference point 300 arrives as if this light came from asurface patch that is offset in range. The introduction of projectedreference point 300 influences the speckle pattern intensity in a mannerthat carries useful information about object 105. In reference to FIG.3b , if the delay between paths 305 and 310 is sufficiently large, thencopy 350 and inverted copy 355 produced by scattering points associatedwith projected reference point 300 will be offset from and will notoverlap with the central autocorrelation 360 that would be formed byobject 105 without projected reference point 300. Thus projectedreference point 300 produces a copy and inverted copy that appearautomatically in the autocorrelation and can be extracted from theautocorrelation by a regional image selection technique.

Because projected reference point 300 has a finite size and mayilluminate numerous scattering cells on the surface of object 105, thethree-dimensional image formed by the projected reference point willcontain closely spaced copies, thus causing a blur and decreasing thelateral resolution of the image. In one embodiment of the invention, theimage quality is improved by moving fiber end 335 sideways in such a wayas to move the projected reference point to different regions of thesurface of object 105. By doing so, favorable positions of the projectedreference point may be determined where the spot falls on a region ofthe surface producing a glint or a specular reflection. In oneembodiment, fiber motion is produced with a piezoelectric actuator.Since the backscattered light from a glint or specular area of thesurface often comes from a small region of the surface, the effectivesize of projected reference point 300 is reduced, producing athree-dimensional image with improved lateral resolution. In oneembodiment, regions of the surface that are likely to produce projectedreference points 300 with small lateral extent are determined byscanning the beam and monitoring the return signal intensity while doingso. Areas producing bright returns are likely to be associated withglints or specular reflections. In another embodiment an autocorrelationis produced for the projected reference point alone by switching offlight from path 305 with an optical switch. The autocorrelation of theprojected reference indicates its extent in each direction and positionsof projected reference point 300 may be chosen that produce aneffectively small projected reference point 300. In another embodimentthe three-dimensional image formed by projected reference point 300 isused as input or as a starting point for the reconstruction of thethree-dimensional image from the autocorrelation as described below. Inyet another embodiment, the separate measurement of the autocorrelationof the projected reference point is used to deconvolve the spread causedby the finite size of the reference point, thus improving thethree-dimensional image.

In addition to the methods of creating a projected reference point asdescribed above, it is also understood that projected reference pointsmay be created by other methods of creating recognizable referencepoints including, but not limited to utilizing multiple radiationsources.

Also with reference to FIG. 3b , in another embodiment of the invention,the sampling rate of the speckle pattern is reduced by a factor ofapproximately two in both the x and in the y directions so that aliasingin these directions, as indicated by dashed lines is allowed to occur incentral autocorrelation 360. This aliasing does not affect copy 350 andinverted copy 355 so that fewer detector elements, by a factor ofapproximately two in both the x and the y directions of detector array115, can be used to obtain the same resolution.

In reference to FIG. 4, in an additional embodiment of the invention,curved surface 400 produces a virtual reference point that is offset inrange from region of interest 405 of object 105, thus forming copy 410and inverted copy 415 that are sufficiently separated in range fromcentral autocorrelation 420 and can be extracted from theautocorrelation by a regional image selection technique (indicated bythe dashed box) to form a three-dimensional image of region of interest405 without the need for reconstructing the image from theautocorrelation. In one embodiment of the invention, the curved surfaceis transparent and lies in front of region of interest 405, thusallowing the bulk of the light in illumination beam 108 to strike regionof interest 405. In one embodiment curved surface 400 is the cornea ofan eye. In one embodiment the region of interest 405 is an iris. Inanother embodiment, the region of interest includes the retina,floaters, or internal structures of the eye, such as the lens. In oneembodiment a three-dimensional iris scan is produced for the purpose ofrecognizing and identifying people for control access. In anotherembodiment, the three-dimensional iris scan is used to recognize orcategorize people at long ranges. In another embodiment of the inventionthe three-dimensional images of the eye are used for ophthalmologicalpurposes.

Many other embodiments of the invention utilizing a curved surface 400are anticipated. Additional embodiments of the curved surface include,but are not limited to, a curved component of a three-dimensionalmicroscope, a lens, a transparent cover, and a cover glass on a specimenbox. These additional embodiments have applicable uses for purposes suchas, but not limited to, ophthalmology, biometric authentication,microscopy, remote imaging and other imaging purposes.

The systems and methods of this invention also provide for comparisonsof autocorrelations. In one embodiment of the invention, theautocorrelation of object 105 is produced for two different viewingangles that have many scattering points on the surface in common.Different viewing angles may be obtained, for example, from relativerotations of object 105 with respect to illumination beam 108. The twoautocorrelations are rotated such that points in the autocorrelationthat are produced by points that are common to both views overlap. Thetwo autocorrelations are then compared point by point. Regions that aresupported in one autocorrelation and not the other are related to pointsthat have become shadowed or have become visible due to the relativerotation of the object between measurements. These points may also arisefrom specular returns that are present for only one of the measurements.For example, if a bright point or specular reflection occurs in onemeasurement and not the other, then the difference between these twomeasurements provides an estimate of a dominant copy, which is taken asa three-dimensional image of the object. In some cases thethree-dimensional image may consist of multiple dominant copies. In thiscase, reconstruction techniques disclosed below may be used to furtherrefine the three-dimensional image.

In another embodiment of the invention, autocorrelations are producedfor two similar objects 105 that are to be compared or for the sameobject to be compared at different times with the possibility of changesoccurring over time. The autocorrelations to be compared are rotated soas to maximize the area of overlap and change detection between the twoautocorrelations is implemented. The variations between autocorrelationsare related to differences in physical features of the objects. Physicalfeatures, or partial representations, of an object produce their owncontribution to the autocorrelation. It can be verified that a physicalfeature is not contained in an object if all the support points in theautocorrelation that correspond to the partial representationrepresenting the feature are not contained in the autocorrelation of theobject. Thus, presence or absence of features of objects in a scene canbe sensed using the autocorrelation of the scene and knowledge of theautocorrelation of the physical features of interest.

Referring to FIG. 5, the concepts underlying one embodiment of systemsand methods to shift-and-compare autocorrelations to construct arepresentation of an object can begin to be illustrated. In oneembodiment of the invention, a three-dimensional image of object 105 isformed from the autocorrelation corresponding to that object byreconstructing an estimate of a copy or of an inverted copy from theautocorrelation. With reference to FIG. 5, for simplicity in explainingthe concepts underlying the invention, the autocorrelation of atwo-dimensional object is considered. These concepts generalize tohigher dimensions so that it is sufficient to consider two dimensions inillustrating the concepts. Autocorrelation 500 is a two-dimensionalautocorrelation with the vertical axis representing range and thehorizontal axis representing a lateral dimension. Autocorrelation 500contains a set of support points, one of which, point 505 is indicatedin the drawing. Shifted autocorrelation 510 is formed by shiftingautocorrelation 500 such that the origin of autocorrelation 500 lies onshift 505. Regions of overlap between the shifted and originalautocorrelations are displayed in overlap region 515. It can be proventhat for any shift point 505 which is a support point of autocorrelation500, that the overlap region 515 contains an intact copy 520 andinverted copy 525. In this illustration, copy 520 corresponds to a faceprofile. The shift-and-compare process, is thus a powerful tool foreliminating points in the autocorrelation that do not lie on a copy andforming a reconstruction by successive shift-and-compare operations. Aslong as subsequent shifts lie on the copy being reconstructed, it isguaranteed in the noiseless case that only points lying on copies otherthan the one being reconstructed will be discarded by a sequence ofshift-and-compare operations. Because every practical measurement has anoise floor, it is possible for some points in the autocorrelation tofall below the noise floor and to be clipped by a threshold functionwhen estimating the support of the autocorrelation function. Therefore,it is advantageous in some situations to choose shifts corresponding tobright points on the copy and to truncate the process by not allowingpoints below a certain brightness to be used as shifts. The compareoperation may take many forms. Three forms are described in thedefinitions section, giving rise to the terms shift-and-intersect,shift-and-min, and shift-and-rank. A series of shift-and-compareoperations using good shifts tends to eliminate additional points notlying on the copy being reconstructed.

A shift-and-compare operation can be performed numerically in many ways.In one embodiment of the invention, the autocorrelation is treated as athree-dimensional array of numbers. In another embodiment, the size ofthe array that needs to be considered when comparing previous shifts isreduced on-the-fly as the process continues so as to be equal to thebounding box corresponding to the intersection of all prior shiftedarrays, including the original array. Reducing the boundary box reducesthe number of computations that must be performed when doing thecomparison operation. In another embodiment of the invention, survivingpoints are listed sequentially including the coordinate positions andthe brightness. As the process continues, this operation becomes moreefficient because the length of the list decreases and fewer and fewerpoints need to be compared.

The crux of the reconstruction problem is to determine a list of goodshifts that is effective in eliminating unwanted points. FIG. 6 is ahigh-level flow chart describing the reconstruction process. The firststep of the process 600 is to construct the autocorrelation from thespeckle pattern intensity. The second step 605 is to select one or moregood shift candidates. The third step 610 is to perform ashift-and-compare operation on the autocorrelation function using one ormore good shifts, thus producing a partial reconstruction. The processthen iterates between selecting shifts 605 and performingshift-and-compare operations 610. After the first shift, theshift-and-compare operation compares the shifted autocorrelation withthe partial reconstruction formed by prior shifts. Once a set ofshift-and-compare operations have been formed, the final step is tooutput a three-dimensional representation of the object 615.

FIG. 7 is a lower-level flow chart showing additional details in thereconstruction process for one realization of the invention. The stepsshown and the order that they are shown in is only one example of howthe various processing tools and components illustrated as blocks in theflow chart can be put together to form a reconstruction. Many of thesteps in the process may not be necessary and can be entirely skippedfor many reconstruction problems. There are numerous paths through theflow chart, and entire blocks within the chart can be bypassed. FIG. 7is meant to describe a set of tools and to illustrate how they can beused together to solve some of the most difficult reconstructionproblems. Many typical reconstruction problems can be solved by using asmall subset of the steps illustrated on the chart.

At a high level, FIG. 7 can be broken into six major sections.Initialization steps are described in blocks 700-707. Termination stepsare described in blocks 765-770. A method for selecting a first shiftand finding additional good shifts based on vector matching is describedin blocks 710-720. Blocks 730-740 describe a method for selecting a setof shifts that are likely to lie on a bright copy and determining whichof these shifts are self-consistent. It includes an error testing stepto help guard against the possibility of introducing shifts that are notgood shifts. Blocks 750-756 describe methods for selecting shiftcandidates that are likely to be good shifts. An error testing step mayalso be included here. Block 760 pertains to methods to combinedifferent starts that may be produced by making different choices of theinitial shift in block 710 or by starting with different sets ofsimultaneous shifts in block 730. For each of the three occurrences ofshift-and-compare (blocks 712, 736, and 750) a choice can be made as towhich compare operation to use. Common choices in order of computationaldifficulty are shift-and-intersect, shift-and-min, and shift-and-rank.

The individual blocks in FIG. 7 are now described in greater detail. Thefirst step in the process, block 700, is to form an autocorrelation froma speckle measurement or to be given an autocorrelation as the startingpoint. Block 705 pertains to finding preferred shifts according to oneor more of the selection criteria listed in block 707, including localpeaks, separated peaks, perimeter points, extreme perimeter points,potent points, spreading points, and low interference points. This listof criteria is not exhaustive, but only provides examples of the type ofselection rules that might be used. The objective of the selection rulesis to provide first shift candidates that eliminate many points and arelikely to lie on bright or dominant copies in the autocorrelation.

In reference to FIG. 8, in another embodiment, first shifts are chosenfrom a list of potent points taken from perimeter points 800. Shiftcandidates 805, 810, and 815, which are taken from a list of perimeterpoints and extreme perimeter points are tested to form partialreconstructions 825, 830, and 835, respectively. The number of survivingpoints for each shift are calculated and displayed. Shift 805 producesby far the fewest number of survivors and is used as the initial shiftin this embodiment.

Different values of the first shift may produce different unique starts,labeled A₁, A₂, A₃, etc., that may be combined later in block 760 toprovide a larger set of good shifts that will eliminate many more pointsfrom the autocorrelation than one start used alone. In block 710 a firstshift is selected from the list of preferred shift candidates. In block712, a shift-and-compare operation is performed using this shift. Next,in block 714, a perimeter point or extreme perimeter point is chosenfrom the list of preferred shift candidates. This perimeter point isthen used in a vector matching operation 716 to determine a cluster ofshift candidates. The process of vector matching is illustrated in FIG.9. Support point 900 is taken from a set of perimeter points 905 and isselected to form the test vector 915. The ends of test vector 915 arethe origin 910 and the support point 900. One point of vector 915 isplaced on each point in partial reconstruction 920. Points are tagged inthe partial reconstruction where both ends of the vector overlap with asupport point of partial reconstruction 920. Point pair 925 and 930 andpoint pair 935 and 940 indicate positions where the vector ends align.In this case the vector only overlaps in two places. One of theseoverlaps corresponds to a fit to the copy and the other overlapcorresponds to a fit to the inverted copy. Point pair 935 and 940 ischosen to break symmetry. In this example, running a shift-and-compareoperation with points 935 and 940 as shifts almost entirely eliminatesthe other copy, leaving a partial reconstruction 945 with a greatlyreduced number of points that closely resembles the object.

Returning to FIG. 7, in many cases, the vector fits into multiple placesin a region of points, and if symmetry has not been broken, it is onlyguaranteed that two of these fits correspond to good shifts. In order toguarantee that a mistake is not made in the selection of shiftcandidates at this point, the entire cluster of shifts can be run usinga union operation or a maximum operation on the individual shiftcandidates in the cluster of shifts. Since at least one of the shifts inthe cluster of shifts is guaranteed to be a good shift, performing theunion operation or the maximum operation on this combination of shiftsguarantees that no points on the copy are eliminated. If vector matchingproduces a cluster with more than two vector matches after the firstshift, then one has the option to apply the entire set as a unionoperation and keep both the copy and inverted copy in tact, or to breakthe symmetry at this point. The symmetry of having two copies survivingmay aid in the selection of good shifts. In the embodiment illustratedin FIG. 7, symmetry is broken at this point. In one embodiment, symmetryis broken by dividing the pairs of shift candidates in the cluster ofshifts into two halves such that one half is point symmetric about theorigin with respect to the other half. In this dividing process, pointpairs corresponding to a given vector match are kept together as a unit.Because of the symmetry of the resulting two sets of shift candidates,each group is guaranteed to contain at least one good shift. Thus,symmetry may be broken without making an error. After the unionoperation 720 is completed, there is a choice as to iterate theprocedure using a new perimeter vector or to drop down to block 750 andselect an additional shift using one or more of the constraints in block754. Typically, since the process of vector shifting 716 and performinga union operation 720 is guaranteed not to produce a mistake, it ispreferred to iterate this loop. If stagnation occurs, then furtherprogress can likely be made by dropping down to block 750.

In block 750, additional shifts are selected according to one or more ofthe rules for selecting addition shift candidates listed in block 754.The list in block 754 is not exhaustive, but merely illustrates the typeof rules that might be employed in the selection process. The listedrules include selecting new shifts from the partial reconstruction thatare also in the preferred candidate list 707, selecting bright points inthe partial reconstruction, or selecting points based on the opacityconstraint, geometrical assumptions, or vector matching. Ashift-and-compare operation 750 is then performed using a new shift, andthe option exists to test for errors 756 at various stages of theiteration. It is not necessary to test during every loop of theiteration. If an error has been made, then it will eventually becomeobvious in terms of too many points being eliminated so that a simpletest will suffice. In order to minimize time, there should be a properbalance between the time spent testing and the time spent exploringpaths. If an error has been made, then the process can be picked up at aprior point where it is believed that no errors have been made.

Now, the branch of the flow chart is explained that begins with block730. This branch relates to the possibility of creating additional newstarts labeled B₁, B₂, B₃, . . . . The first step in this branch,selecting a set of bright points 730, refers to selecting points fromthe list of preferred shift candidates that are likely to lie on one ofthe brightest copies. For low interference events, which are more likelyto occur for points not too close to the origin, the brightest copiestend to produce separated peaks, i.e., local peaks in theautocorrelation that are larger than nearest neighbors within a givenradius. In one embodiment of the invention, block 730 is implemented bymaking a list of the brightest of these separated peaks. FIG. 10illustrates the process of forming a consistency matrix 732 andreordering the consistency matrix to produce a large block ofself-consistent points 734. The row headings 1000 and column headings1005 of consistency matrix 732 refer to indices that represent aninitial ordering of the points for which the matrix is generated. Blackelements 1010 of consistency matrix 732 indicate point pairs that areself-consistent, the indices of the point pairs corresponding to rowheadings 1000 and column headings 1005. In one embodiment, the initialstep in reordering the elements of the matrix is to count the number oftimes each point is self-consistent with a point in the list. Thesenumbers are indicated by row summation 1015 and column summation 1020.In reordered consistency matrix 734, the row headings 1025 and columnheadings 1030 have been reordered according to the ranking of the rowsummation 1015 (or column summation 1020). The row headings 1025 (orcolumn headings 1030) in the resulting contiguous block 1035 refer tothe indices of the set of self-consistent points used inshift-and-compare operation 736.

In one embodiment reordering is achieved by counting the number ofpoints in the list each point is self-consistent with. The rows andcolumns of the matrix are then reordered to place those that areconsistent with more points at the top of the list. In an additionalstep, the points are reordered to produce a large, preferably thelargest possible, contiguous block in the top left corner of the matrix.The points corresponding to this contiguous list are then allself-consistent and are likely to lie on a copy. These shifts can be runin parallel in shift-and-compare operation 736, and tested for errors738. More bright points are then selected from the intersection of thepartial reconstruction and other preferred points that are likely to lieon the copy. It is possible that shifts creep into the list that,although being self-consistent with the other shifts, do not lie on acopy and are not self-consistent with other points on the copy.Therefore, in order to maximize the size of the self-consistent list, itmay be necessary to allow points to be removed to test whether a largerself-consistent list could be formed in the absence of these points. Theconsistency matrix approach may generate an adequate reconstructionwithout being tied into the other branches of the flow chart. In thiscase the process flow goes directly to the filter reconstruction block765, assuming the reconstruction has extra points in a column to beremoved by the range filtering process. Assuming that different startshave been generated and none of the starts produced a satisfactoryreconstruction by itself, the starts can be combined into a larger startin block 760. As part of the process of combining starts, ashift-and-compare operation is performed to produce a partialreconstruction that is smaller than the partial reconstruction obtainedby the individual starts. If the partial reconstruction produced bycombining starts is satisfactory, then the process goes to block 765. Ifit is not, the process returns to block 710 or block 730 for moreiterations.

If more than one start has been generated, these starts may be combinedinto a single start in block 760. The offset necessary for combiningstarts is determined by a process known as cross-matching. Sincemultiple starts can be combined sequentially one at a time, it issufficient to describe the process of combining two starts. Consideringa first and a second start, the process is performed by first matchingthe first start with the second partial reconstruction. Matching isachieved by selecting a point from the first start to serve as anindicator point for defining the relative position of that start.Matches between the first start and the second partial reconstructionare then determined by translating the indicator point on the firststart such that it lies sequentially on each point in the secondreconstruction. A match occurs if every point in the first startoverlaps with a support point of the second partial reconstruction.(This description in terms of choosing an indicator point and settingthe point on a support point in the partial reconstruction is only meantto be a description of the logic for one embodiment of the invention.The invention anticipates the use of algorithms such as, but not limitedto the cross-correlation algorithm implemented through the use of FFTs,that perform these types of operations in parallel.) If the first startoverlaps with the second partial reconstruction in only one place, thenthe offset must be correct and the points on the second partialreconstruction where this overlap with the first start occurs are addedto the second start to produce a combined start with more elements. Anew partial reconstruction with fewer elements can then be formed byperforming a shift-and-compare operation using all the points in thecombined start as shifts. If the first start overlaps with the secondpartial reconstruction in more than one location, then the combinationof all of the points in the second partial reconstruction where theseoverlaps occur forms a cluster of shift candidates. There is now theoption of running a union operation, or a maximum operation, on thiscluster of shift candidates to produce a new partial reconstruction withfewer points.

In a second optional step of the cross-matching operation, the aboveprocedure is repeated with the second start being matched to the firstpartial reconstruction. Again, if a match occurs at only one location,then the points in the first partial reconstruction that overlap withthe second shifted start are added to the first start to produce acombined start with more elements. A new partial reconstruction withfewer elements can then be formed by performing a shift-and-compareoperation using all the points in the combined start as shifts. Again,if the second start overlaps with the first partial reconstruction inmore than one location the combination of all of the points in thesecond partial reconstruction where these overlaps occur forms a clusterof shift candidates. Once again, there is the option of running a unionoperation, or a maximum operation, on this cluster of shift candidatesto produce a new partial reconstruction with fewer points.

A further step in the process of cross-matching to combine starts is tocompare the list of offsets of the first start that produces a completeoverlap with support points of the second partial reconstruction withthe list of offsets of the second start that produces a complete overlapwith support points of the first partial reconstruction. Since the trueoffset that produces a combined start depends on which start is beingused as the base in the comparison, and since the magnitude of theoffset must be the same in both cases for the offset that correctlycombines the two starts, a reduced list of potential offsets can beobtained by switching the sign of the coordinates of the offsets for oneof the two lists and forming the intersection. The correct offset forcombining the two lists must be contained in the intersection of thesetwo lists. If the intersection contains only one offset, then thisoffset must be the correct offset. Thus, the two starts can be combinedinto a new start with more elements. The sign of the offset should bechosen that produces the brighter copy. If the intersection containsmore than one offset, then each of these offsets can be applied toproduce a cluster of shift candidates. A partial reconstruction withfewer elements can then be formed by performing a union operation or amaximum operation on the cluster of shift candidates.

A final optional step that can be formed on starts or combined starts isto translate the start so that it falls on the brightest copy. This canbe accomplished by performing a three-dimensional cross-correlation. Thelocation of the maximum point in the cross-correlation indicates theoffset that is likely to translate the start to the brightest copy. Thesame technique can be used to translate a partial reconstruction to thebrightest copy once the number of extra points in the partialreconstruction has been sufficiently reduced.

A final optional step in the reconstruction process is to filter thereconstruction in range 765 to eliminate residual multiple values inrange. If a bright copy is being reconstructed, a very effective meansof eliminating residual range points is to keep the brightest point ineach column. Other examples of effective means for smoothing in rangeare to form the center of mass of the residual points in a column, totake the median point, and to smooth by taking into account the positionof residual points in neighboring columns.

It will be appreciated that the processes and methods disclosed hereinmay be realized in hardware, software, or any combination of thesesuitable for the 3D imaging and reconstruction techniques describedherein. This includes realization in one or more microprocessors,microcontrollers, embedded microcontrollers, programmable digital signalprocessors or other programmable device, along with internal and/orexternal memory. This may also, or instead, include one or moreapplication specific integrated circuits, programmable gate arrays,programmable array logic components, or any other device or devices thatmay be configured to process electronic signals. It will further beappreciated that a realization may include computer executable codecreated using a structured programming language such as C, an objectoriented programming language such as C++, or any other high-level orlow-level programming language (including assembly languages, hardwaredescription languages, and database programming languages andtechnologies) that may be stored, compiled or interpreted to run on oneof the above devices, as well as heterogeneous combinations ofprocessors, processor architectures, or combinations of differenthardware and software. At the same time, processing may be distributedacross devices such as a radiation source, a sensor and/or computer in anumber of ways or all of the functionality may be integrated into adedicated, standalone image capture device. All such permutations andcombinations are intended to fall within the scope of the presentdisclosure.

It will also be appreciated that means for performing the stepsassociated with the processes described above may include any suitablecomponents of the imaging apparatus described above, along with anysoftware and/or hardware suitable for controlling operation of same.

While the invention has been disclosed in connection with certainpreferred embodiments, other embodiments will be recognized by those ofordinary skill in the art, and all such variations, modifications, andsubstitutions are intended to fall within the scope of this disclosure.Thus, the invention is to be understood with reference to the followingclaims, which are to be interpreted in the broadest sense allowable bylaw.

What is claimed is:
 1. An imaging apparatus comprising: a first sourceof radiation configured to create a first beam of radiation having awavelength and illuminate an object to produce a projected referencespot at a first location on the object; a second source of radiationconfigured to create a second beam of radiation that is mutuallycoherent with the first beam of radiation and simultaneously illuminatethe object to produce a wider-area illumination beam at a secondlocation on the object; a controller configured to control thewavelength; at least one sensor at a location relative to the objectconfigured to determine at least one speckle pattern intensity for atleast two instances of the wavelength; a processor configured to receiveinformation from the at least one sensor; and the processor configuredto utilize the projected reference spot and the at least two instancesof the wavelength to produce a three-dimensional representation of aregion of the second location on the object.
 2. The imaging apparatus ofclaim 1 wherein the second source of radiation is the same as the firstsource of radiation and further comprising a beam splitter configured toproduce the first beam of radiation and the second beam of radiationfrom the first source of radiation.
 3. The imaging apparatus of claim 2wherein the first source of radiation is a tunable laser.
 4. The imagingapparatus of claim 1 wherein the projected reference spot is a focusedbeam of radiation.
 5. The imaging apparatus of claim 1 wherein the firstlocation on the object and the second location on the object overlap. 6.The imaging apparatus of claim 1 further comprising a means for movingthe first location on the object with respect to the second location onthe object.
 7. The imaging apparatus of claim 1 wherein the controlleris further configured to control an optical path delay between the firstbeam of radiation and the second beam of radiation.
 8. The imagingapparatus of claim 7 wherein the optical path delay is controlled by anoptical fiber length.
 9. The imaging apparatus of claim 1 wherein theprocessor further comprises means for constructing at least oneautocorrelation and executing a regional image selection technique toconstruct the three-dimensional representation of the region of thesecond location on the object.
 10. A method for constructing athree-dimensional image of an object comprising the steps of: outputtinga first beam of radiation having a wavelength and illuminating an objectto produce a projected reference spot at a first location on the object;outputting a second beam of radiation that is mutually coherent with thefirst beam of radiation and simultaneously illuminating the object toproduce a wider-area illumination beam at a second location on theobject; changing the wavelength; determining at least one specklepattern intensity for at least two wavelengths with at least one sensor;receiving information from the at least one sensor; utilizing theprojected reference spot and at least two instances of the wavelength toproduce a three-dimensional representation of a portion of the secondlocation on the object.
 11. The method of claim 10 further comprising:splitting a beam from a coherent source of radiation to produce thefirst beam of radiation and the second beam of radiation.
 12. The methodof claim 11 wherein the splitting the beam from the coherent source ofradiation comprises splitting the beam from a tunable laser.
 13. Themethod of claim 10 further comprising focusing the first beam ofradiation on the first location on the object to form the projectedreference spot.
 14. The method of claim 10 further comprisingoverlapping the first location on the object and the second location onthe object.
 15. The method of claim 10 further comprising moving thefirst location on the object with respect to the second location on theobject.
 16. The method of claim 10 further comprising controlling anoptical path delay between the first beam of radiation and the secondbeam of radiation.
 17. The method of claim 16 wherein the controllingthe optical path delay further comprises controlling an optical fiberlength.
 18. The method of claim 10 wherein the utilizing the projectedreference spot and at least two instances of the wavelength to produce athree-dimensional representation of a portion of the second location onthe object comprises constructing at least one autocorrelation andexecuting a regional image selection technique.
 19. A method forconstructing a three-dimensional image of an object comprising the stepsof: splitting a laser beam having a wavelength to form a first beam ofradiation and a second beam of radiation; the first beam of radiationilluminating an object to produce a projected reference spot at a firstlocation on the object; the second beam of radiation illuminating theobject to produce a wider-area illumination beam at a second location onthe object; the first beam of radiation and the second beam of radiationsimultaneously illuminating the object; controlling an optical pathdelay between the first beam of radiation and the second beam ofradiation; controlling the wavelength; determining at least one specklepattern intensity for at least two instances of the wavelength with atleast one sensor; receiving information from the at least one sensor;utilizing the projected reference spot and the at least two instances ofthe wavelength and the optical path delay to produce a three-dimensionalrepresentation of a region of the object illuminated by the second beamof radiation.
 20. The method of claim 19 further comprising overlappingthe reference spot and the wider-area illumination beam on a surface ofthe object.